Use of the SF-36v2 Health Survey as a Screen for Risk of Major Depressive Disorder in a US Population-based Sample and Subgroup With Chronic Pain.

Journal Article (Journal Article)

STUDY OBJECTIVES: To assess the feasibility of using the SF-36v2 mental health (MH) and mental component summary (MCS) scores for classification of risk for major depressive disorder (MDD), and to determine cut-off scores based on the sensitivity and specificity in a general US representative sample, and a chronic pain subpopulation. METHODS: Data were analyzed from the 2013 US National Health and Wellness Survey (adults 18 y old and above; N=75,000), and among a chronic pain subpopulation (n=6679). Risk of MDD was a score ≥10 on the Patient Health Questionnaire (PHQ-9). Logistic regression modeling was used to predict at risk for MDD and receiver operating characteristic curves were produced. RESULTS: The total sample had MH scores of 48.8 and MCS scores of 48.9, similar to the normative US population mean. Percent of respondents with a PHQ-9≥10 were 15.0% and 29.1% for the total sample and chronic pain subpopulation, respectively. Cut-off scores (PHQ-9≥10) in the total sample for the MH and MCS were 43.0 and 46.0, respectively. Specificities for the MH and MCS were 77.8% and 76.1%; sensitivities were 84.9% and 88.1%, respectively. Among the subpopulation with chronic pain, cut-off scores for the MH and MCS were 40.4 and 43.1, respectively. Corresponding specificities for the MH and MCS were 77.9% and 73.9%; sensitivities were 78.3% and 83.4%, respectively. CONCLUSIONS: The SF-36v2 was found to have sufficient specificity and sensitivity to categorize participants at risk for MDD. If no depression questionnaire is available, it is feasible to use the SF-36v2 to characterize the MH of populations.

Full Text

Duke Authors

Cited Authors

  • Bell, JA; daCosta DiBonaventura, M; Witt, EA; Ben-Joseph, R; Reeve, BB

Published Date

  • February 2017

Published In

Volume / Issue

  • 55 / 2

Start / End Page

  • 111 - 116

PubMed ID

  • 27517330

Electronic International Standard Serial Number (EISSN)

  • 1537-1948

Digital Object Identifier (DOI)

  • 10.1097/MLR.0000000000000617


  • eng

Conference Location

  • United States